DA0-002 Data Concepts and Environments Practice Question
A data engineer is designing a data warehouse for a retail company. The fact table must record each sale transaction, including product ID, store ID, date, and quantity sold. The product details (name, category, price) are stored in a separate table. This design is an example of which data modeling concept?
⚠ Common exam trap
A common mix-up: candidates confuse star schema with snowflake schema, but the key differentiator is whether dimension tables are further normalized (snowflake) or kept denormalized (star), and this question's single product table clearly indicates a star schema.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
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Star schema
This design is a classic star schema, where a central fact table (sales transactions) contains foreign keys to dimension tables (product, store, date). The fact table stores quantitative measures (quantity sold) and foreign keys, while dimension tables hold descriptive attributes (product name, category, price). This separation optimizes query performance for OLAP workloads by reducing joins and enabling straightforward aggregations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Star schema
Why this is correct
A star schema satisfies this design because it centres a fact table of sale transactions — product ID, store ID, date, quantity — surrounded by denormalised dimension tables such as product, holding name, category and price. The separate product table is precisely that dimension, keeping descriptive attributes out of the fact grain.
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Data lake
Why it's wrong here
A data lake stores raw, unmodelled data in its native format; it defines no fact table, dimension keys or star-style structure. It is tempting because both hold large volumes of retail data, and would be correct if the requirement were landing raw source files for later processing rather than a modelled dimensional warehouse.
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Normalization
Why it's wrong here
Normalization is a broader process, not a specific schema.
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Snowflake schema
Why it's wrong here
A snowflake schema normalises dimensions into multiple related tables, so product name, category and price would be split across separate lookup tables rather than held in one product dimension. It is tempting because it reduces redundancy, and would be correct if the product dimension were deliberately normalised into related sub-tables.
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